GARDRec: Decision-Level Graph Grounding for Large Language Model Recommendation

• URL: http://arxiv.org/abs/2608.00669v1
• Date: Sat, 01 Aug 2026 13:44:04 GMT
• Title: GARDRec: Decision-Level Graph Grounding for Large Language Model Recommendation
• Abstract summary: We propose emphGARDRec, a Graph-grounded Adaptive Reasoning and Decision-aware Recommendation framework for next-item ranking.<n>GARDRec constructs semantic-structural item representations from textual node features and graph propagation.<n>Experiments show that GARDRec generally improves candidate-ranking performance over representative baselines.
• Score: 19.913416708367503
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Large language models (LLMs) offer new opportunities for recommendation by interpreting item descriptions, user instructions, and external knowledge through natural-language prompts. However, existing graph-augmented LLM recommenders often use knowledge graphs mainly as prompt-level evidence, leaving ranking decisions weakly constrained by structured user-item relations. This is problematic for next-item recommendation, where the model must compare candidates under the same user context while preserving temporal preference, collaborative signals, and attribute matches. To address this issue, we propose \emph{GARDRec}, a Graph-grounded Adaptive Reasoning and Decision-aware Recommendation framework for LLM-based next-item ranking. GARDRec constructs semantic-structural item representations from textual node features and graph propagation, derives personalized graph contexts from temporally weighted histories and first-order neighborhoods, and aligns graph-derived representations with a frozen LLM through continuous multimodal prompts. Explicit interaction and matching features are injected through late-stage decision branches, while inter-candidate attention and restricted generative likelihood support final ranking. Experiments on three public benchmarks with multiple LLM backbones show that GARDRec generally improves candidate-ranking performance over representative baselines. Ablation and diagnostic analyses verify the contributions of graph projection, neighborhood retrieval, explicit decision features, ranking loss, and generative calibration.

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